Hybrid Evolutionary Algorithms for Constraint Satisfaction Problems: Memetic Overkill? B.G.W. Craenen Napier University 10 Colinton Road Edinburgh, EH10 5DT United Kingdom b.craenen@napier.ac.uk A.E. Eiben Vrije Universiteit Amsterdam De Boelelaan 1081a 1081 HV, Amsterdam The Netherlands gusz@cs.vu.nl Abstract- We study a selected group of hybrid EAs for solving CSPs, consisting of the best performing EAs from the literature. We investigate the contri- bution of the evolutionary component to their per- formance by comparing the hybrid EAs with their “de-evolutionarised” variants. The experiments show that “de-evolutionarising” can increase performance, in some cases doubling it. Considering that the problem domain and the algorithms are arbitrarily selected from the “memetic niche”, it seems likely that the same effect occurs for other problems and algorithms. Therefore, our conclusion is that after designing and building a memetic algorithm, one should perform a verification by comparing this algorithm with its “de-evolutionarised” variant. 1 Introduction During the last decade, many researchers have adopted the use of heuristics within an evolutionary algorithm (EA) be- cause of the positive effect on algorithm performance. Ad- vocated already in the mid 90ies (cf. [22]), such algorithms, called hybrid EAs or memetic algorithms, offer the best of both words: the robustness of the EA because of the un- biased population-based search and the directed search im- plied by the heuristic bias. As for algorithm performance, it is assumed and expected that the hybrid EA performs bet- ter than the EA alone and the heuristic alone. Supported by significant practical evidence, the contemporary view within the EC community considers this memetic approach the most successful in treating challenging (combinatorial) optimisation problems. In this paper we add a critical note to this opinion. In particular, we design and perform targeted experiments to assess the contribution of the evolutionary component of hybrid EAs to good results. The way to test this is to “de- evolutionarise” the EAs and see whether the results get bet- ter or worse. Technically speaking the question is how to “remove evolution” from an EA. For a solid answer one should identify the essential features of EAs for which it holds that after removing or switching off these features, the resulting algorithm would not qualify for being evolu- tionary. To this end, there are three obvious candidates for belonging to these essential features: Namely, the usage of: • a population of candidate solutions; • variation operators, crossover and mutation; and • natural selection, that is selection based on fitness. Our work, as reported here, is based on the third answer for the following reasons. Considering the role of the pop- ulation, it is true that, in general, EAs use a population of more than one candidate solution. However, there are many successful variants, where the population size is only one, think for instance of evolution strategies [2, 10, 27]. As for the variation operators, we can observe that some move-operator in the search space is always necessary for generate-and-test methods. Hence, the sole presence of the variation operators is not EA specific. The speciality of EAs is often related to the use of crossover for mixing informa- tion of two or more candidate solutions. Nevertheless, there are many EAs that do not use crossover, or any other form of recombination, for instance in evolutionary programming, cf. [10, 15, 14]. Considering natural selection, recall that there are two selection steps in the general EA framework: parent selec- tion and survivor selection. For either of them we say that it represents natural selection if a fitness-based bias is in- corporated. Note, that an EA does not need to have natural selection in both steps. For instance, generational GAs use only parent selection (and all children survive), while ES use only survivor selection (and parents are chosen uniform randomly). However, an EA must have fitness-bias in at least one of these steps. If neither parent selection nor sur- vivor selection are performed by using fitness-bias (e.g., by uniform random selection) then we have no natural selec- tion and obtain random walk. Based on these considerations we “de-evolutionarise” the EAs by switching off natural selection. Technically, we set all selection operators to uniform random, that is, can- didate solutions are selected by selecting them randomly while each candidate solution has an equal probability of being selected. 2 CSPs and our generator The Constraint Satisfaction Problem (CSP) is a well-known satisfiability problem that is NP-complete ([26]). Infor- mally, the CSP is defined as a set of variables X and a set constraints C between these variables. Variables are only assigned values from their respective domains, denoted as D. Assigning a value to a variable is called labelling a vari-